Update README.md

README.md

@@ -152,13 +152,16 @@ language:
 tags:
 - biology
 - protein
-- instruction dataset
 - instruction tuning
+- AI4Science
+- Life Science
+- LLM
 pretty_name: Open Protein Instructions(OPI)
 size_categories:
 - 1M<n<10M
 task_categories:
 - text-generation
+- question-answering
 ---
 
 ![image.png](./OPI_logo.png)
@@ -180,36 +183,16 @@ Reference:
 - **Point of Contact:** 
 
 ### Dataset Overview
+We are excited to announce the release of the OPI dataset, a curated collection of instructions covering 9 tasks for adapting LLMs to protein biology, containing 1.61M samples. The dataset is designed to advance LLM-driven research in the field of protein biology. We welcome contributions and enhancements to this dataset from the community.
 
 Open Protein Instructions(OPI) is the initial part of Open Biology Instructions(OBI) project, together with the subsequent Open Molecule Instructions(OMI), Open DNA Instructions(ODI), Open RNA Instructions(ORI) and Open Single-cell Instructions (OSCI). OBI is a project which aims to fully leverage the potential ability of Large Language Models(LLMs), especially the scientific LLMs like Galactica, to facilitate research in AI for Life Science community. While OBI is still in an early stage, we hope to provide a starting point for the community to bridge LLMs and biological domain knowledge.
 
-We are excited to announce the release of the OPI dataset, a curated collection of instructions covering 9 tasks for adapting LLMs to protein biology. The dataset is designed to advance LLM-driven research in the field of protein biology. We welcome contributions and enhancements to this dataset from the community.
-
-**Accessing the OPI dataset:**
-The OPI dataset is organized into the three subfolders—AP, KM, and SU—by in the [OPI_DATA](./OPI_DATA/) directory within this repository, where you can find a subset for each specific task as well as the full dataset file: [OPI_full_1.61M_train.json](./OPI_DATA/OPI_full_1.61M_train.json). f you want to merge all or several training data files of the tasks into one single training data file, please do like this:
-
-## Dataset Examples
-
-**An example of OPI training data:**
-```
-instruction: 
-    What is the EC classification of the input protein sequence based on its biological function?
-input:                         
-    MGLVSSKKPDKEKPIKEKDKGQWSPLKVSAQDKDAPPLPPLVVFNHLTPPPPDEHLDEDKHFVVALYDYTAMNDRDLQMLKGEKLQVLKGTGDWWLARS
-    LVTGREGYVPSNFVARVESLEMERWFFRSQGRKEAERQLLAPINKAGSFLIRESETNKGAFSLSVKDVTTQGELIKHYKIRCLDEGGYYISPRITFPSL
-    QALVQHYSKKGDGLCQRLTLPCVRPAPQNPWAQDEWEIPRQSLRLVRKLGSGQFGEVWMGYYKNNMKVAIKTLKEGTMSPEAFLGEANVMKALQHERLV
-    RLYAVVTKEPIYIVTEYMARGCLLDFLKTDEGSRLSLPRLIDMSAQIAEGMAYIERMNSIHRDLRAANILVSEALCCKIADFGLARIIDSEYTAQEGAK
-    FPIKWTAPEAIHFGVFTIKADVWSFGVLLMEVVTYGRVPYPGMSNPEVIRNLERGYRMPRPDTCPPELYRGVIAECWRSRPEERPTFEFLQSVLEDFYT
-    ATERQYELQP
-output: 
-    2.7.10.2
-```
-- **An example of OPI testing data:**
-```
-{"id": "seed_task_0", "name": "EC number of price dataset from CLEAN", "instruction": "Return the EC number of the protein sequence.", "instances": [{"input": "MAIPPYPDFRSAAFLRQHLRATMAFYDPVATDASGGQFHFFLDDGTVYNTHTRHLVSATRFVVTHAMLYRTTGEARYQVGMRHALEFLRTAFLDPATGGYAWLIDWQDGRATVQDTTRHCYGMAFVMLAYARAYEAGVPEARVWLAEAFDTAEQHFWQPAAGLYADEASPDWQLTSYRGQNANMHACEAMISAFRATGERRYIERAEQLAQGICQRQAALSDRTHAPAAEGWVWEHFHADWSVDWDYNRHDRSNIFRPWGYQVGHQTEWAKLLLQLDALLPADWHLPCAQRLFDTAVERGWDAEHGGLYYGMAPDGSICDDGKYHWVQAESMAAAAVLAVRTGDARYWQWYDRIWAYCWAHFVDHEHGAWFRILHRDNRNTTREKSNAGKVDYHNMGACYDVLLWALDAPGFSKESRSAALGRP", "output": "5.3.1.7"}], "is_classification": false}
-```
+## OPI Dataset Construction Pipeline
+The OPI dataset is curated on our own by extracting key information from [Swiss-Prot](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue&query=%2A) database. The following figure shows the general construction process.
+![image.png](./OPI_data.png)
 
 ## OPI Dataset Folder Structure
+The OPI dataset is organized into the three subfolders—AP, KM, and SU—by in the [OPI_DATA](./OPI_DATA) directory within this repository, where you can find a subset for each specific task as well as the full dataset file: [OPI_full_1.61M_train.json](./OPI_DATA/OPI_full_1.61M_train.json). 
 ```
 ./OPI_DATA/
 └── SU
@@ -269,10 +252,31 @@ output:
             └── gene_name_to_cancer_train.json
 ```
 
-## OPI Dataset Construction Pipeline
+## Dataset Examples
 
-The OPI dataset is curated on our own by extracting key information from [Swiss-Prot](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue&query=%2A) database. The following figure shows the general construction process.
-![image.png](./OPI_data.png)
+**An example of OPI training data:**
+```
+instruction: 
+    What is the EC classification of the input protein sequence based on its biological function?
+input:                         
+    MGLVSSKKPDKEKPIKEKDKGQWSPLKVSAQDKDAPPLPPLVVFNHLTPPPPDEHLDEDKHFVVALYDYTAMNDRDLQMLKGEKLQVLKGTGDWWLARS
+    LVTGREGYVPSNFVARVESLEMERWFFRSQGRKEAERQLLAPINKAGSFLIRESETNKGAFSLSVKDVTTQGELIKHYKIRCLDEGGYYISPRITFPSL
+    QALVQHYSKKGDGLCQRLTLPCVRPAPQNPWAQDEWEIPRQSLRLVRKLGSGQFGEVWMGYYKNNMKVAIKTLKEGTMSPEAFLGEANVMKALQHERLV
+    RLYAVVTKEPIYIVTEYMARGCLLDFLKTDEGSRLSLPRLIDMSAQIAEGMAYIERMNSIHRDLRAANILVSEALCCKIADFGLARIIDSEYTAQEGAK
+    FPIKWTAPEAIHFGVFTIKADVWSFGVLLMEVVTYGRVPYPGMSNPEVIRNLERGYRMPRPDTCPPELYRGVIAECWRSRPEERPTFEFLQSVLEDFYT
+    ATERQYELQP
+output: 
+    2.7.10.2
+```
+- **An example of OPI testing data:**
+```
+{"id": "seed_task_0", "name": "EC number of price dataset from CLEAN", "instruction": "Return the EC number of the protein sequence.", "instances": [{"input":
+"MAIPPYPDFRSAAFLRQHLRATMAFYDPVATDASGGQFHFFLDDGTVYNTHTRHLVSATRFVVTHAMLYRTTGEARYQVGMRHALEFLRTAFLDPATGGY
+AWLIDWQDGRATVQDTTRHCYGMAFVMLAYARAYEAGVPEARVWLAEAFDTAEQHFWQPAAGLYADEASPDWQLTSYRGQNANMHACEAMISAFRATGERR
+YIERAEQLAQGICQRQAALSDRTHAPAAEGWVWEHFHADWSVDWDYNRHDRSNIFRPWGYQVGHQTEWAKLLLQLDALLPADWHLPCAQRLFDTAVERGWD
+AEHGGLYYGMAPDGSICDDGKYHWVQAESMAAAAVLAVRTGDARYWQWYDRIWAYCWAHFVDHEHGAWFRILHRDNRNTTREKSNAGKVDYHNMGACYDVL
+LWALDAPGFSKESRSAALGRP", "output": "5.3.1.7"}], "is_classification": false}
+```
 
 ## License
 The dataset is licensed under a Creative Commons Attribution Non Commercial 4.0 License. The use of this dataset should also abide by the original [License & Disclaimer](https://www.uniprot.org/help/license) and [Privacy Notice](https://www.uniprot.org/help/privacy) of UniProt.